2026-07-02 04:00 UTCOriginal source2 min readUpdated: 2026-07-02 07:57 UTC

RareDxR1: Autonomous Medical Reasoning for Rare Disease Diagnosis Beyond Human Annotation

RareDxR1 is an end-to-end reasoning-centric large language model designed for open-domain rare disease diagnosis directly from unstructured clinical notes. It bypasses traditional pipeline-based phenotype extraction or retrieval-augmented generation by synergizing knowledge internalization with autonomous evolutionary learning, and employs Reflection-Enhanced Reasoning Sampling and dual-level curriculum reinforcement learning to improve diagnostic accuracy. Experiments show state-of-the-art results across multiple benchmarks.

SourcearXiv AIAuthor: Deyang Jiang, Haoran Wu, Ziyi Wang, Yiming Rong, Yunlong Zhao, Ye Jin, Bo Xu

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Key points

RareDxR1 diagnoses rare diseases directly from unstructured clinical notes without requiring structured phenotypes.
It overcomes limitations of RAG and phenotype restriction through knowledge internalization and autonomous evolutionary learning.
Reflection-Enhanced Reasoning Sampling (RERS) synthesizes expert-level diagnostic trajectories by learning from failures without human annotation.
Achieves state-of-the-art accuracy on multiple benchmarks, marking a breakthrough in open-domain rare disease diagnosis.

Why it matters

This matters because rareDxR1 diagnoses rare diseases directly from unstructured clinical notes without requiring structured phenotypes.

Technical impact

May affect model selection, inference cost, product capability, and evaluation benchmarks.

This panel is AI-generated and reviewed for accuracy.

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[Submitted on 30 Jun 2026]

Title:RareDxR1: Autonomous Medical Reasoning for Rare Disease Diagnosis Beyond Human Annotation

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Abstract:Rare disease differential diagnosis is a critical yet arduous clinical task, requiring physicians to identify precise phenotypes from complex, unstructured patient symptoms and execute intricate reasoning within a vast search space. However, existing AI approaches typically rely on pipeline-based phenotype extraction or retrieval-augmented generation, which suffer from critical information loss due to predefined ontologies, retrieval bottlenecks, and a lack of diagnostic logic. To address these challenges, we introduce RareDxR1, an end-to-end reasoning-centric large language model designed for open-domain rare disease diagnosis directly from unstructured clinical notes. We design a progressive end-to-end training framework by synergizing knowledge internalization with autonomous evolutionary learning, thereby bypassing reliance on structured phenotypes and closed-set decision-making. To overcome the limitations of RAG and phenotype restriction, we enabled the deep internalization of fragmented rare-disease knowledge directly into the model's parameters. Moreover, to bridge the gap between model generation and expert reasoning, we propose Reflection-Enhanced Reasoning Sampling (RERS), a strategy that synthesizes expert-level diagnostic trajectories by learning from failures without human annotation. Additionally, we propose a dual-level curriculum reinforcement learning approach for gradually mastering rare disease diagnosis. Experimental results demonstrate that RareDxR1 achieves state-of-the-art accuracy across different benchmarks, marking a significant breakthrough in open-domain rare disease diagnosis. Our code and dataset will be publicly available.

Comments: 7 pages, 3 figures. Accepted to IEEE International Conference on Multimedia and Expo (ICME) 2026

Subjects:

Artificial Intelligence (cs.AI)

ACM classes: I.2.7; I.2.6; J.3

Cite as: arXiv:2607.00147 [cs.AI]

(or arXiv:2607.00147v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.00147

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Deyang Jiang [view email] [v1] Tue, 30 Jun 2026 20:25:53 UTC (1,143 KB)

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